A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem

2016 ◽  
Vol 101 ◽  
pp. 116-127 ◽  
Author(s):  
Atiye Ghorbani ◽  
Mohammad Reza Akbari Jokar
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Inventory Problem ◽  
Location Routing ◽  
Product Location ◽  
Annealing Algorithm

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2842-2845 ◽  
Author(s):  
Gai Li Du ◽  
Nian Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Location Routing ◽  
Annealing Algorithm

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

A New Solution for Maxterm Problem in Trigonometric Functions by Simulated Annealing Algorithm

2013 ◽  
Vol 4 (2) ◽  
pp. 20-28
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Hadi Najafi ◽  
Kourosh Farahkhah
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Trigonometric Functions ◽  
Matlab Software ◽  
Annealing Algorithm ◽  
Do So

The present paper is an attempt to get total minimum of trigonometric Functions by Simulated Annealing. To do so the researchers ran Simulated Annealing. Sample trigonometric functions and showed the results through Matlab software. According the Simulated Annealing Solves the problem of getting stuck in a local Maxterm and one can always get the best result through the Algorithm.

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